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Exploiting uniqueness: seed-chain-extend alignment on elastic founder graphs.

Nicola Rizzo1, Manuel Cáceres2, Veli Mäkinen1

  • 1Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland.

Bioinformatics (Oxford, England)
|July 15, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Computational pangenomics uses seed-chain-extend heuristics for sequence-to-graph alignment. We developed a novel workflow using indexable elastic founder graphs (iEFGs) for efficient alignment of human chromosome data.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Sequence-to-graph alignment is a key challenge in computational pangenomics.
  • Current methods often rely on heuristic approaches like seed-and-extend or seed-chain-extend.

Purpose of the Study:

  • To implement a complete seed-chain-extend alignment workflow for enhanced sequence-to-graph alignment.
  • To address the theoretical hardness of sequence-to-graph alignment using novel graph structures.

Main Methods:

  • Development of a seed-chain-extend alignment workflow utilizing indexable elastic founder graphs (iEFGs).
  • iEFGs enable linear-time exact searches, overcoming limitations of general graph structures.
  • Demonstration of iEFG construction, seed finding, chaining, and extension at the scale of a human chromosome.

Main Results:

  • Successful implementation of a complete seed-chain-extend alignment workflow.
  • Validation of the approach on a telomere-to-telomere assembled human chromosome.
  • Demonstrated efficiency and scalability of the iEFG-based alignment.

Conclusions:

  • The developed iEFG-based workflow provides an efficient solution for sequence-to-graph alignment.
  • This approach advances computational pangenomics by overcoming the inherent complexity of graph alignment.
  • The methodology is scalable to large genomic datasets, including entire human chromosomes.